Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use Erfan2001/multilingual_tokenized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Erfan2001/multilingual_tokenized with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Erfan2001/multilingual_tokenized")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Erfan2001/multilingual_tokenized") model = AutoModelForSequenceClassification.from_pretrained("Erfan2001/multilingual_tokenized", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e0cbc758cfeda91d35c05bbb7857fa05df47218cd1ac4d07529f0d46fef8f00c
- Size of remote file:
- 712 MB
- SHA256:
- af91253daf76574b155a26f892dd57a84fc0d2ff8f909c377dd730ae098105aa
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